The control of complex dynamical technical systems, (e.g., gas turbines, wind turbines, or other plants), may be optimized by so-called data driven approaches. With that, various aspects of such dynamical systems may be improved. For example, efficiency, combustion dynamics, or emissions for gas turbines may be improved. Additionally, life-time consumption, efficiency, or yaw for wind turbines may be improved.
Modern data driven optimization utilizes machine learning methods for improving control policies (also denoted as control strategies) of dynamical systems with regard to general or specific optimization goals. Such machine learning methods may allow to outperform conventional control strategies. In particular, if the controlled system is changing, an adaptive control approach capable of learning and adjusting a control strategy according to the new situation and new properties of the dynamical system may be advantageous over conventional non-learning control strategies.
However, in order to optimize complex dynamical systems, (e.g., gas turbines or other plants), a sufficient amount of operational data is to be collected in order to find or learn a good control strategy. Thus, in case of commissioning a new plant, upgrading or modifying it, it may take some time to collect sufficient operational data of the new or changed system before a good control strategy is available. Reasons for such changes might be wear, changed parts after a repair, or different environmental conditions.
Known methods for machine learning include reinforcement learning methods that focus on data efficient learning for a specified dynamical system. However, even when using these methods it may take some time until a good data driven control strategy is available after a change of the dynamical system. Until then, the changed dynamical system operates outside a possibly optimized envelope. If the change rate of the dynamical system is very high, only sub-optimal results for a data driven optimization may be achieved since a sufficient amount of operational data may be never available.